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  • 44 hours of instructor-led training
  • 24 hours of self-paced learning videos
  • 4 real-life industry-based projects in the domains of telecom, stock market, etc.
  • Interactive learning with Jupyter notebooks labs
  • Includes concepts of web scraping
  • Includes a free Python basics course

Course description

  • Why learn Data Science with Python?

  • What are the course objectives?

    The Data Science with Python course will furnish you with in-depth knowledge of the various libraries and packages required to perform data analysis, data visualization, web scraping, machine learning and natural language processing using Python. With this Data Science with Python course, you will learn to work with Python packages such as PROC SQL and various statistical procedures such as PROC UNIVARIATE, PROC MEANS, PROC FREQ, and PROC CORP, as well as advanced analytics techniques such as clustering, decision tree, and regression.
     
    The Python for Data Science course is packed with real-life projects focused on customer segmentation, macro calls, attrition analysis, and retail analysis, as well as demos and case studies to give you practical experience in installing and working in the Python environment.
     
    Python has surpassed Java as the top language used to introduce US students to programming and computer science, and 46 percent of data science jobs list Python as a required skill.

  • What skills will you learn?

    This Python for Data Science training course will enable you to:
    Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
    Install the required Python environment and other auxiliary tools and libraries
    Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
    Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions
    Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave
    Perform data analysis and manipulation using data structures and tools provided in the Pandas package
    Gain expertise in machine learning using the Scikit-Learn package
    Gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline
    Use the Scikit-Learn package for natural language processing
    Use the matplotlib library of Python for data visualization
    Extract useful data from websites by performing web scrapping using Python
    Integrate Python with Hadoop, Spark and MapReduce

  • Who should take this Python for Data Science course?

    There is a booming demand for skilled data scientists across all industries that make this course suited for participants at all levels of experience. We recommend this Data Science with Python training particularly for the following professionals:
    Analytics professionals who want to work with Python
    Software professionals looking to get into the field of analytics
    IT professionals interested in pursuing a career in analytics
    Graduates looking to build a career in analytics and data science
    Experienced professionals who would like to harness data science in their fields
    Anyone with a genuine interest in the field of data science
     
    Prerequisites: There are no prerequisites for this Data Science with Python course. The Python basics course included with this program provides additional coding guidance.
     

  • What projects are included in this Python for Data Science certification course?

    The course includes four real-world, industry-based projects. Successful evaluation of one of the following projects is a part of the certification eligibility criteria:
     
    Project 1: NYC 311 Service Request Analysis
    Telecommunication: Perform a service request data analysis of New York City 311 calls. You will focus on data wrangling techniques to understand patterns in the data and visualize the major complaint types.
     
    Project 2: MovieLens Dataset Analysis
    Engineering: The GroupLens Research Project is a research group in the Department of Computer Science and Engineering at the University of Minnesota. The researchers of this group are involved in several research projects in the fields of information filtering, collaborative filtering and recommender systems. Here, we ask you to perform an analysis using the Exploratory Data Analysis technique for user datasets.
     
    Project 3: Stock Market Data Analysis
    Stock Market: As a part of this project, you will import data using Yahoo data reader from the following companies: Yahoo, Apple, Amazon, Microsoft and Google. You will perform fundamental analytics, including plotting, closing price, plotting stock trade by volume, performing daily return analysis, and using pair plot to show the correlation between all of the stocks.
     
    Project 4: Titanic Dataset Analysis
    Hazard: On April 15, 1912, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This tragedy shocked the world and led to better safety regulations for ships. Here, we ask you to perform an analysis using the exploratory data analysis technique, in particular applying machine learning tools to predict which passengers survived the tragedy.

Course preview

    • Lesson 00 - Course Overview 04:34
      • 0.1 Course Overview04:34
    • Lesson 01 - Data Science Overview 20:27
      • 1.1 Introduction to Data Science08:42
      • 1.2 Different Sectors Using Data Science05:59
      • 1.3 Purpose and Components of Python05:02
      • 1.4 Quiz
      • 1.5 Key Takeaways00:44
    • Lesson 02 - Data Analytics Overview 18:20
      • 2.1 Data Analytics Process07:21
      • 2.2 Knowledge Check
      • 2.3 Exploratory Data Analysis(EDA)
      • 2.4 EDA-Quantitative Technique
      • 2.5 EDA - Graphical Technique00:57
      • 2.6 Data Analytics Conclusion or Predictions04:30
      • 2.7 Data Analytics Communication02:06
      • 2.8 Data Types for Plotting
      • 2.9 Data Types and Plotting02:29
      • 2.10 Knowledge Check
      • 2.11 Quiz
      • 2.12 Key Takeaways00:57
    • Lesson 03 - Statistical Analysis and Business Applications 23:53
      • 3.1 Introduction to Statistics01:31
      • 3.2 Statistical and Non-statistical Analysis
      • 3.3 Major Categories of Statistics01:34
      • 3.4 Statistical Analysis Considerations
      • 3.5 Population and Sample02:15
      • 3.6 Statistical Analysis Process
      • 3.7 Data Distribution01:48
      • 3.8 Dispersion
      • 3.9 Knowledge Check
      • 3.10 Histogram03:59
      • 3.11 Knowledge Check
      • 3.12 Testing08:18
      • 3.13 Knowledge Check
      • 3.14 Correlation and Inferential Statistics02:57
      • 3.15 Quiz
      • 3.16 Key Takeaways01:31
    • Lesson 04 - Python Environment Setup and Essentials 23:58
      • 4.1 Anaconda02:54
      • 4.2 Installation of Anaconda Python Distribution (contd.)
      • 4.3 Data Types with Python13:28
      • 4.4 Basic Operators and Functions06:26
      • 4.5 Quiz
      • 4.6 Key Takeaways01:10
    • Lesson 05 - Mathematical Computing with Python (NumPy) 30:31
      • 5.1 Introduction to Numpy05:30
      • 5.2 Activity-Sequence it Right
      • 5.3 Demo 01-Creating and Printing an ndarray04:50
      • 5.4 Knowledge Check
      • 5.5 Class and Attributes of ndarray
      • 5.6 Basic Operations07:04
      • 5.7 Activity-Slice It
      • 5.8 Copy and Views
      • 5.9 Mathematical Functions of Numpy05:01
      • 5.10 Assignment 01
      • 5.11 Assignment 01 Demo03:55
      • 5.12 Assignment 02
      • 5.13 Assignment 02 Demo03:16
      • 5.14 Quiz
      • 5.15 Key Takeaways00:55
    • Lesson 06 - Scientific computing with Python (Scipy) 23:35
      • 6.1 Introduction to SciPy06:57
      • 6.2 SciPy Sub Package - Integration and Optimization05:51
      • 6.3 Knowledge Check
      • 6.4 SciPy sub package
      • 6.5 Demo - Calculate Eigenvalues and Eigenvector01:36
      • 6.6 Knowledge Check
      • 6.7 SciPy Sub Package - Statistics, Weave and IO05:46
      • 6.8 Assignment 01
      • 6.9 Assignment 01 Demo01:20
      • 6.10 Assignment 02
      • 6.11 Assignment 02 Demo00:55
      • 6.12 Quiz
      • 6.13 Key Takeaways01:10
    • Lesson 07 - Data Manipulation with Pandas 47:34
      • 7.1 Introduction to Pandas12:29
      • 7.2 Knowledge Check
      • 7.3 Understanding DataFrame05:31
      • 7.4 View and Select Data Demo05:34
      • 7.5 Missing Values03:16
      • 7.6 Data Operations09:56
      • 7.7 Knowledge Check
      • 7.8 File Read and Write Support00:31
      • 7.9 Knowledge Check-Sequence it Right
      • 7.10 Pandas Sql Operation02:00
      • 7.11 Assignment 01
      • 7.12 Assignment 01 Demo04:09
      • 7.13 Assignment 02
      • 7.14 Assignment 02 Demo02:34
      • 7.15 Quiz
      • 7.16 Key Takeaways01:34
    • Lesson 08 - Machine Learning with Scikit–Learn 1:02:10
      • 8.1 Machine Learning Approach03:57
      • 8.2 Steps 1 and 201:00
      • 8.3 Steps 3 and 4
      • 8.4 How it Works01:24
      • 8.5 Steps 5 and 601:54
      • 8.6 Supervised Learning Model Considerations00:30
      • 8.7 Knowledge Check
      • 8.8 Scikit-Learn02:10
      • 8.9 Knowledge Check
      • 8.10 Supervised Learning Models - Linear Regression11:19
      • 8.11 Supervised Learning Models - Logistic Regression08:43
      • 8.12 Unsupervised Learning Models10:40
      • 8.13 Pipeline02:37
      • 8.14 Model Persistence and Evaluation05:45
      • 8.15 Knowledge Check
      • 8.16 Assignment 01
      • 8.17 Assignment 0105:45
      • 8.18 Assignment 02
      • 8.19 Assignment 0205:14
      • 8.20 Quiz
      • 8.21 Key Takeaways01:12
    • Lesson 09 - Natural Language Processing with Scikit Learn 49:03
      • 9.1 NLP Overview10:42
      • 9.2 NLP Applications
      • 9.3 Knowledge check
      • 9.4 NLP Libraries-Scikit12:29
      • 9.5 Extraction Considerations
      • 9.6 Scikit Learn-Model Training and Grid Search10:17
      • 9.7 Assignment 01
      • 9.8 Demo Assignment 0106:32
      • 9.9 Assignment 02
      • 9.10 Demo Assignment 0208:00
      • 9.11 Quiz
      • 9.12 Key Takeaway01:03
    • Lesson 10 - Data Visualization in Python using matplotlib 32:46
      • 10.1 Introduction to Data Visualization08:02
      • 10.2 Knowledge Check
      • 10.3 Line Properties
      • 10.4 (x,y) Plot and Subplots10:01
      • 10.5 Knowledge Check
      • 10.6 Types of Plots09:34
      • 10.7 Assignment 01
      • 10.8 Assignment 01 Demo02:23
      • 10.9 Assignment 02
      • 10.10 Assignment 02 Demo01:47
      • 10.11 Quiz
      • 10.12 Key Takeaways00:59
    • Lesson 11 - Web Scraping with BeautifulSoup 52:27
      • 11.1 Web Scraping and Parsing12:50
      • 11.2 Knowledge Check
      • 11.3 Understanding and Searching the Tree12:56
      • 11.4 Navigating options
      • 11.5 Demo3 Navigating a Tree04:22
      • 11.6 Knowledge Check
      • 11.7 Modifying the Tree05:38
      • 11.8 Parsing and Printing the Document09:05
      • 11.9 Assignment 01
      • 11.10 Assignment 01 Demo01:55
      • 11.11 Assignment 02
      • 11.12 Assignment 02 demo04:57
      • 11.13 Quiz
      • 11.14 Key takeaways00:44
    • Lesson 12 - Python integration with Hadoop MapReduce and Spark 40:39
      • 12.1 Why Big Data Solutions are Provided for Python04:55
      • 12.2 Hadoop Core Components
      • 12.3 Python Integration with HDFS using Hadoop Streaming07:20
      • 12.4 Demo 01 - Using Hadoop Streaming for Calculating Word Count08:52
      • 12.5 Knowledge Check
      • 12.6 Python Integration with Spark using PySpark07:43
      • 12.7 Demo 02 - Using PySpark to Determine Word Count04:12
      • 12.8 Knowledge Check
      • 12.9 Assignment 01
      • 12.10 Assignment 01 Demo02:47
      • 12.11 Assignment 02
      • 12.12 Assignment 02 Demo03:30
      • 12.13 Quiz
      • 12.14 Key takeaways01:20
    • Project 1 18:36
      • Project 1 Stock Market Data Analysis18:36
    • Project 2 20:06
      • Project 02
      • Main project 0220:06
    • Course Feedback
      • Course Feedback
    • Lesson 00 - Course Overview 04:44
      • 0.1 Introduction00:13
      • 0.2 Offerings00:07
      • 0.3 Course Objectives00:29
      • 0.4 Course Overview00:21
      • 0.5 Target Audience00:27
      • 0.6 Course Prerequisites00:11
      • 0.7 Need of Python00:49
      • 0.8 Python vs. Rest Other Languages00:25
      • 0.9 Value to the Professionals00:16
      • 0.10 Value to the Professionals (contd.)00:31
      • 0.11 Value to the Professionals (contd.)00:24
      • 0.12 Lessons Covered00:23
      • 0.13 Conclusion00:08
    • Lesson 01 - Introduction to Python 28:15
      • 1.1 Introduction00:12
      • 1.2 Objectives00:16
      • 1.3 An Introduction to Python01:27
      • 1.4 Features of Python00:44
      • 1.5 The History of Python00:27
      • 1.6 Releases00:33
      • 1.7 Installation on Ubuntu-based Machines01:00
      • 1.8 Installation on Windows00:59
      • 1.9 Demo-Install and Run Python00:08
      • 1.10 Demo-Install and Run Python14:17
      • 1.11 Example of a Python Program01:08
      • 1.12 Modes of Python00:27
      • 1.13 Batch Script Mode00:29
      • 1.14 Demo-Run Python in the Batch Mode00:05
      • 1.15 Demo-Run Python in the Batch Mode01:14
      • 1.16 Interpreter Mode00:46
      • 1.17 Demo-Run Python in the Interpreter Mode00:05
      • 1.18 Demo-Run Python in the Interpreter Mode00:31
      • 1.19 Indentation in Python00:49
      • 1.20 Indentation in Python (contd.)00:26
      • 1.21 Writing Comments in Python01:06
      • 1.22 Business Scenario00:23
      • 1.23 Quiz
      • 1.24 Summary00:33
      • 1.25 Conclusion00:10
    • Lesson 02 - Python Data Types 19:34
      • 2.1 Python Data Types00:10
      • 2.2 Objectives00:18
      • 2.3 Variables00:52
      • 2.4 Types of Variables01:09
      • 2.5 Types of Variables-String01:07
      • 2.6 Types of Variables-Numeric Types00:34
      • 2.7 Types of Variables-Boolean Variables00:34
      • 2.8 Types of Variables-Boolean Variables (contd.)00:35
      • 2.9 Types of Variables-List00:24
      • 2.10 Adding Elements to a List00:48
      • 2.11 Accessing the Elements of a List01:09
      • 2.12 Types of Variables-Dictionary00:30
      • 2.13 Adding Elements to a Dictionary00:50
      • 2.14 Accessing the Elements of a Dictionary00:12
      • 2.15 Dictionary Methods00:32
      • 2.16 Dictionary Methods (contd.)00:30
      • 2.17 Operators00:21
      • 2.18 Opeators (contd.)00:10
      • 2.19 Logical Operators00:44
      • 2.20 Logical Operators (contd.)00:47
      • 2.21 Logical Operators (contd.)00:39
      • 2.22 Arithmetic Operations on Numeric Values00:58
      • 2.23 Order of Operands01:03
      • 2.24 Operators on Strings01:03
      • 2.25 Variables Comparison01:06
      • 2.26 Variables Comparison (contd.)01:05
      • 2.27 Variables Comparison (contd.)00:33
      • 2.28 Quiz
      • 2.29 Summary00:41
      • 2.30 Conclusion00:10
    • Lesson 03 - Control Statements 09:27
      • 3.1 Introduction00:10
      • 3.2 Objectives00:13
      • 3.3 Pass Statements00:15
      • 3.4 Conditional Statements00:45
      • 3.5 Types of Conditional Statements00:18
      • 3.6 If Statements00:28
      • 3.7 If…Else Statements00:49
      • 3.8 If…Else If Statements01:06
      • 3.9 If…Else If…Else Statements00:18
      • 3.10 Nested If Statements00:38
      • 3.11 Demo-Use “If…Else” Statement00:05
      • 3.12 Demo-Use “If…Else” Statement02:12
      • 3.13 In Clause00:56
      • 3.14 Ternary Operators00:44
      • 3.15 Quiz
      • 3.16 Summary00:21
      • 3.17 Conclusion00:09
    • Lesson 04 - Loops 08:10
      • 4.1 Introduction00:10
      • 4.2 Objectives00:12
      • 4.3 Loops in Python00:37
      • 4.4 Range Function00:28
      • 4.5 For Loop00:35
      • 4.6 For Loop (contd.)00:23
      • 4.7 While Loop00:35
      • 4.8 Nested Loop00:50
      • 4.9 Demo-Create Loops00:05
      • 4.10 Demo-Create Loops02:21
      • 4.11 Break Statements00:48
      • 4.12 Continue Statements00:36
      • 4.13 Quiz
      • 4.14 Summary00:22
      • 4.15 Conclusion00:08
    • Lesson 05 - Functions 09:27
      • 5.1 Introduction00:10
      • 5.2 Objectives00:13
      • 5.3 Introduction to Functions00:49
      • 5.4 Creating Functions00:49
      • 5.5 Calling Functions00:43
      • 5.6 Arguments and Return Statement01:28
      • 5.7 Variable-Length Arguments00:53
      • 5.8 Variable-Length Arguments (contd.)00:33
      • 5.9 Recursion00:43
      • 5.10 Demo-Create a Function00:05
      • 5.11 Demo-Create a Function02:19
      • 5.12 Quiz
      • 5.13 Summary00:33
      • 5.14 Conclusion00:09
    • Lesson 06 - Classes 11:23
      • 6.1 Introduction00:10
      • 6.2 Objectives00:14
      • 6.3 Classes01:39
      • 6.4 Objects00:33
      • 6.5 Creating a Basic Class00:35
      • 6.6 Accessing Variables of a Class00:39
      • 6.7 Adding Functions to a Class00:40
      • 6.8 Built-in Class Attributes00:37
      • 6.9 Init Function00:38
      • 6.10 Example of Defining and Using a Class00:42
      • 6.11 Example of Defining and Using a Class (contd.)00:27
      • 6.12 Demo-Create a Class00:05
      • 6.13 Demo-Create a Class03:34
      • 6.14 Quiz
      • 6.15 Summary00:40
      • 6.16 Conclusion00:10
    • Lesson 07 - Imports and Modules 12:01
      • 7.1 Introduction00:11
      • 7.2 Objectives00:16
      • 7.3 Modules00:54
      • 7.4 Creating Modules00:18
      • 7.5 Using Modules00:14
      • 7.6 Using Modules (contd.)01:10
      • 7.7 Using Modules (contd.)00:27
      • 7.8 Using Modules (contd.)00:26
      • 7.9 Python Interpreter Module Search00:57
      • 7.10 Demo-Create and Import a Module00:06
      • 7.11 Demo-Create and Import a Module02:24
      • 7.12 Namespace and Scoping00:57
      • 7.13 Dir() Function00:29
      • 7.14 Dir() Function (contd.)00:23
      • 7.15 Global and Local Functions00:31
      • 7.16 Reload a Module00:48
      • 7.17 Packages in Python00:46
      • 7.18 Quiz
      • 7.19 Summary00:34
      • 7.20 Conclusion00:10
    • Statistics Essential for Data Science 30:50
      • Statistics for Data Science30:50
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Exam & certification FREE PRACTICE TEST

  • How do I earn my Simplilearn certificate?

    To become a Certified Data Scientist with Python, you must fulfill the following criteria:
    • Complete one project out of the two provided in the course. Submit the deliverables of the project in the LMS which will be evaluated by our lead trainer
    • Score a minimum of 60% in any one of the two simulation tests
    • Complete 85% of the course
    • Attend one complete batch.
     
    Note: When you have completed the course, you will receive a three-month experience certificate for implementing the projects using Python. It is mandatory that you fulfill both the criteria (completion of any one project and passing the online exam with minimum score of 60%) to become a certified data scientist.

Reviews

Shoeb Mohammad
Shoeb Mohammad Analyst at Accenture, Delhi

I had joined the Data Science certification from Simplilearn. The course content was really good. The trainer puts a lot of efforts into explaining every detail which made the learning very absorbing. The customer support is always available whenever you need help. I actually feel one step forward towards my goal. Thank you.

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Gaurav Dubey
Gaurav Dubey Associate Consultant at Syntel, Pune

Prior to joining Data Science course with Simplilearn, I had little knowledge about it. The certification helped me to understand the Machine Learning, Web Scraping, Natural Language Processing in detail. The trainer was very helpful and was always there to guide me in every step. The certification helped me to enhance my career from Software Engineer to Associate Consultant with a salary hike. I am planning to take a few more course from Simplilearn in future.

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Jatin Alwani
Jatin Alwani Student at Lovely Professional University, Jalandhar

I have enrolled for Data Science certification from Simplilearn. The course materials are great and the trainers are also very helpful. The industry-based project is the best part of the course. Simplilearn is better than any others in the market.

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FAQs

  • What are the system requirements?

    To run Python, your system must fulfill the following basic requirements:
    • 32 or 64-bit Operating System
    • 1GB RAM 
    The instruction uses Anaconda and Jupyter notebooks. The e-learning videos provide detailed instruction on how to install them.

  • Who are our instructors and how are they selected?

    All of our highly qualified trainers are industry experts with at least 10-12 years of relevant teaching experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.

  • What are the modes of training offered for this Python for Data Science course?

    Live Virtual Classroom or Online Classroom: In online classroom training, you have the convenience of attending the course remotely from your desktop via video conferencing to enhance your productivity and reduce the time spent away from work or home.
     
    Online Self-Learning: In this mode, you will receive lecture videos and can proceed through the course at your convenience.
     
    WinPython portable distribution is the open source environment on which all hands-on exercises will be performed. Instructions for installation will be given during the training.

  • What if I miss a class?

    Simplilearn provides recordings of each class so you can review them as needed before the next session.

  • Can I cancel my enrollment? Will I get a refund?

    Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, you can view our Refund Policy.

  • Who provides the certification?

    At the end of the training, subject to satisfactory evaluation of the project as well as passing the online exam (minimum score 80%), you will receive a certificate from Simplilearn stating that you are a certified data scientist with Python.

  • Are there any group discounts for classroom training programs?

    Yes, we have group discount packages for classroom training programs. Contact Help & Support to learn more about the group discounts.

  • How do I enroll for the Data Science with Python online training?

    You can enroll for this training on our website and make an online payment using any of the following options: 
    • Visa Credit or Debit Card
    • MasterCard
    • American Express
    • Diner’s Club
    • PayPal 
    Once payment is received you will automatically receive a payment receipt and access information via email.

  • What is Global Teaching Assistance?

    Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.

  • What is covered under the 24/7 Support promise?

    We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us.

    Our Delhi address

    Simplilearn Solutions Pvt Ltd, 2nd Floor, KLJ Tower North, B-5 District Centre, Netaji Subhash Place, Wazirpur, New Delhi – 110034, India, Call us at: 1800-102-9602

    • Disclaimer
    • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.